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A Compact Dual-Broadband Pattern Diversity Antenna for 5G and Wi-Fi Applications 一种适用于5G和Wi-Fi应用的紧凑型双宽带分集天线
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-25 DOI: 10.1002/dac.70276
Wenfei Yin, Zhiwen Deng, Jiacheng Si, Salam Khamas

This paper presents a compact dual-broadband pattern diversity antenna designed for 5G and Wi-Fi applications. The proposed design combines a pair of short and long monopole structures with a radiating patch to achieve omnidirectional radiation in the lower band (2.53–3.31 GHz) and bidirectional radiation in the upper band (5.49–6.06 GHz). By leveraging mode superposition, the antenna maintains stable gain across both frequency ranges, with measured realized gains of 2.29 and 2.95 dBi, respectively. The structure features wide impedance bandwidths of 26.7% and 9.9%, while maintaining a low-profile form factor of 0.013λL. Experimental results align well with simulations, confirming the antenna's effectiveness for multi-system coverage. Due to its compact size and enhanced performance, this antenna is well suited for wireless communication systems.

本文提出了一种适用于5G和Wi-Fi应用的紧凑型双宽带方向分集天线。本设计将一对长、短单极子结构与一个辐射贴片相结合,实现了下频段(2.53 ~ 3.31 GHz)的全向辐射和上频段(5.49 ~ 6.06 GHz)的双向辐射。通过利用模式叠加,天线在两个频率范围内保持稳定的增益,测量的实现增益分别为2.29和2.95 dBi。该结构具有26.7%和9.9%的宽阻抗带宽,同时保持0.013λL的低外形系数。实验结果与仿真结果吻合良好,验证了该天线对多系统覆盖的有效性。由于其紧凑的尺寸和增强的性能,这种天线非常适合无线通信系统。
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引用次数: 0
Minimum Mean Square Error—Successive Interference Cancellation for Cyclic Interleaved Frequency Division Multiplexing 循环交错频分复用的最小均方误差-逐次干扰消除
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-24 DOI: 10.1002/dac.70273
G. Anuthirsha, S. Lenty Stuwart

Cyclic interleaving serves as an effective technique to counteract channel fading. Recently, a successive interference cancellation (SIC) method has been introduced for cyclic interleaved frequency division multiplexing (CIFDM). Although CIFDM-SIC successfully eliminates a major portion of the multipath interference, a significant challenge persists due to the semiorthogonality of the codes, resulting in residual interference. Furthermore, the SIC amplifies noise, resulting in performance degradation. In light of these issues, we propose a novel minimum mean square error-SIC (MMSE-SIC) method for CIFDM. This method is meticulously designed to eliminate residues at each stage of the SIC while concurrently minimizing noise enhancement. The proposed CIFDM-MMSE-SIC demonstrates superior bit error rate performance compared with the current CIFDM-SIC across various realistic scenarios.

循环交织是对抗信道衰落的有效技术。近年来,针对循环交错频分复用(CIFDM),提出了一种逐次干扰消除(SIC)方法。尽管CIFDM-SIC成功地消除了大部分多径干扰,但由于编码的半正交性,仍然存在一个重大挑战,导致残留干扰。此外,SIC放大了噪声,导致性能下降。针对这些问题,我们提出了一种新的CIFDM最小均方误差- sic (MMSE-SIC)方法。该方法经过精心设计,可以在SIC的每个阶段消除残留,同时最小化噪声增强。与现有的CIFDM-SIC相比,所提出的CIFDM-MMSE-SIC在各种实际场景中具有优越的误码率性能。
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引用次数: 0
Advanced Correlation-Based Techniques for CFO and Channel Estimation in Multiuser OFDMA Systems With RIS Deployment RIS多用户OFDMA系统中基于相关的CFO和信道估计技术
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-22 DOI: 10.1002/dac.70265
Tsui-Tsai Lin, Fuh-Hsin Hwang

We present a simple estimation technique for carrier frequency offset (CFO) and uplink channel in multiuser orthogonal frequency division multiplexing access (OFDMA) systems with reconfigurable intelligent surface (RIS) assistance. Based on the block-impulsive pilot feature, our approach considers a correlation-based method for conducting CFO estimation on the received signal derived from cyclic prefix data, providing robustness against multiuser interference and channel variation. Channel estimation is formulated utilizing all the CFO-compensated signals, which requires only a few number of complex multiplication operations. Compared to the existing approach of the reference, the proposed ameliorated technique offers enhanced estimation performance for our scheme, which is validated through a theoretical analysis of the mean squared error of the proposed estimation. Computer simulations have been conducted to confirm its superior performance and reliability.

提出了一种基于可重构智能曲面(RIS)辅助的多用户正交频分复用(OFDMA)系统中载波频偏(CFO)和上行信道的简单估计技术。基于块脉冲导频特征,我们的方法考虑了一种基于相关的方法,对来自循环前缀数据的接收信号进行CFO估计,提供了对多用户干扰和信道变化的鲁棒性。信道估计是利用所有cfo补偿信号制定的,只需要少量复杂的乘法运算。与文献中已有的方法相比,本文提出的改进技术提高了方案的估计性能,并通过对所提估计的均方误差的理论分析验证了这一点。通过计算机仿真验证了其优越的性能和可靠性。
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引用次数: 0
Priority-Based QoS Aware Routing Protocol for Wireless Body Area Networks 基于优先级的无线体域网络QoS感知路由协议
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-21 DOI: 10.1002/dac.70237
Abdollah Rahimi, Mehdi Jafari Shahbazzadeh, Amid Khatibi Bardsiri

With the progress of technology in the field of wireless communication, wireless body area networks (WBANs) have been introduced. WBANs consist of a number of intelligent biomedical sensor nodes and a special node called sink. Sensors are placed on the patient's body or under the skin, and their task is to check physiological parameters and report to the sink. The resources of these nodes are limited, and communication is subject to various issues. Maintaining the quality of communication due to the high importance of the transmitted data and severe limitations is one of the important issues in WBANs. The important challenge in this area is designing a framework for next-hop node selection with covering the quality needs of the transmitted data. This paper proposes a Priority-Based QoS Aware Routing Protocol for WBANs (PQARP). PQARP is a three-step method. In the first step, the sensors are configured. In the second step, data is separated and marked. In the third step, the proposed PQARP routing is done and the most appropriate intermediate node is selected to send data. The simulation results using NS-2 showed the remarkable capabilities of PQARP in covering quality requirements of different data sent and improving the metrics of end-to-end delay, successful delivery ratio, throughput and energy consumption compared to other similar methods.

随着无线通信领域技术的进步,无线体域网络(wban)被引入。wban由多个智能生物医学传感器节点和一个称为sink的特殊节点组成。传感器被放置在病人的身体上或皮肤下,它们的任务是检查生理参数并向水槽报告。这些节点的资源是有限的,并且通信受到各种问题的影响。由于传输数据的高度重要性和严重的局限性,保持通信质量是无线宽带网络的重要问题之一。该领域的重要挑战是设计一个覆盖传输数据质量需求的下一跳节点选择框架。提出了一种基于优先级的wban感知QoS路由协议(PQARP)。PQARP是一个三步法。第一步,配置传感器。在第二步中,对数据进行分离和标记。第三步,完成提出的PQARP路由,选择最合适的中间节点发送数据。基于NS-2的仿真结果表明,与其他类似方法相比,PQARP在满足不同数据发送的质量要求、改善端到端延迟、成功交付率、吞吐量和能耗等指标方面具有显著的能力。
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引用次数: 0
A Beamforming-Aided DRX Mechanism for Traffic-Aware Energy Optimization in 5G NR-U 基于波束形成辅助DRX机制的5G NR-U交通感知能量优化
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-18 DOI: 10.1002/dac.70251
Mahima Kumar, Anupam Gautam

The rise in demand for higher data rates and more subscribers is fueling mobile data traffic. The limited availability of channel access for data transmission requires user equipment (UE) to wait, which subsequently compromises its energy efficiency. To mitigate power consumption and improve quality of service (QoS) in UE, the discontinuous reception (DRX) mechanism over fifth generation (5G) new radio unlicensed (NR-U) offers a viable solution. Our current work explores this by leveraging DRX within NR-U for standalone (SA) and non-standalone (NSA) deployment modes. We have modeled our approach by introducing a traffic-based analyzer state within our system, which is mathematically investigated by subjecting the analyzed traffic to machine learning (ML)-based beamforming techniques. Furthermore, Q-learning-based reinforcement learning (RL) is implemented in this model to determine optimal DRX strategies under dynamic and stochastic traffic conditions, effectively balancing energy efficiency, delay, and QoS requirements.

对更高数据速率的需求和更多用户的增加正在推动移动数据流量。数据传输通道访问的有限可用性要求用户设备(UE)等待,这随后损害了其能源效率。为了降低用户终端的功耗并提高服务质量(QoS),第五代(5G)新无线电无牌(NR-U)的不连续接收(DRX)机制提供了一个可行的解决方案。我们目前的工作是通过在独立(SA)和非独立(NSA)部署模式中利用NR-U中的DRX来探索这一点。我们通过在系统中引入基于流量的分析器状态来建模我们的方法,通过将分析的流量置于基于机器学习(ML)的波束成形技术中进行数学研究。此外,在该模型中实现了基于q学习的强化学习(RL),以确定动态和随机交通条件下的最优DRX策略,有效地平衡了能效、延迟和QoS需求。
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引用次数: 0
Energy-Aware Mobile Data Collection in Wireless Sensor Networks Using Heuristic Clustering and Path Optimization Techniques 基于启发式聚类和路径优化技术的无线传感器网络能量感知移动数据采集
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-18 DOI: 10.1002/dac.70199
Naween Kumar, Shailendra Pratap Singh, Gandharba Swain, Anand Nayyar

Wireless sensor networks (WSNs) face critical energy constraints that directly affect network longevity, especially when scaled to large deployments. This paper uses a multi-stage process to introduce an energy-efficient data collection framework using mobile data collectors (MDCs). We employ a modified weighted set cover approach to identify a minimal set of strategically placed anchor points (APs), significantly reducing the number of required MDCs. After refining them with an optimized fuzzy C-means clustering and a traffic load balancing technique, these APs ensure coverage for all areas and balance the load among each other. Using Euler graph analysis, the routing paths are determined through the minimum spanning tree and perfect matching approaches. Experimental results demonstrate that our suggested method reduces the amount of MDCs by 28%, the distance traveled by tours by 35%, the average delay in data collection by 10%, and increases data transmission efficiency by 16%. The reported results prove that our suggested method helps gather more data energy efficiently and prolong the lifetime of WSN nodes.

无线传感器网络(wsn)面临着直接影响网络寿命的关键能量限制,尤其是在大规模部署时。本文采用多阶段流程介绍了一种使用移动数据收集器(mdc)的节能数据收集框架。我们采用了一种改进的加权集覆盖方法来确定战略锚点(ap)的最小集,从而显著减少了所需的MDCs的数量。这些ap通过优化的模糊c均值聚类和流量负载均衡技术对其进行细化,确保覆盖所有区域并相互平衡负载。利用欧拉图分析,通过最小生成树和完美匹配方法确定路由路径。实验结果表明,该方法减少了28%的MDCs数量,减少了35%的行程距离,平均数据采集延迟减少了10%,数据传输效率提高了16%。实验结果表明,该方法可以有效地收集更多的数据能量,延长WSN节点的生命周期。
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引用次数: 0
Wearable Sensor Network-Driven Human Activity Recognition via Philippine Eagle Optimized Statistics-Infused Neural Network 基于菲律宾鹰优化统计注入神经网络的可穿戴传感器网络驱动的人类活动识别
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-17 DOI: 10.1002/dac.70260
B. Jaishankar, D. Sobya, V. Porkodi, V. Govindaraj

A crucial area of research in behavior analysis, computer communication, and widespread technology is human activity recognition (HAR). Deep learning (DL) approaches are effectively deployed to forecast human behaviors with periodic information from mobile phones as well as wearable sensors. DL-based methods are very good at identifying activities, but struggle with time series data. In this research work, wearable sensor network-driven human activity recognition via Philippine eagle optimized statistics-infused neural network (WSN-HAR-PEO-SINN) is suggested. First, the input information is gathered from the Wireless Sensor Data Mining (WISDM) dataset. In preprocessing, the flat files are combined to form a data frame using confidence partitioning sampling filtering (CPSF). After preprocessing, the data is segmented using unpaired multiview graph clustering (UMGC). It is used to segment the number of human activities. The divided output is sent to the feature extraction system using quadratic phase S-transform (QPST), which is utilized to gather local characteristics such as skewness, mean, and deviation from the mean, energy, mel-frequency cepstral coefficients, and entropy. Then, the extracted characteristics are entered into a neural connection termed statistics-informed neural network (SINN) to recognize the human activity with wearable sensor data and categorize the data as ambulation-oriented, hand-oriented activity general, and hand-oriented activity eating. Generally speaking, SINN does not incorporate any optimization technique adaptations for identifying the optimal parameters that guarantee precise identification of human activities. To enhance the weight parameter of the SINN approach, which accurately detects human activity using wearable sensor data, Philippine eagle optimization (PEO) is suggested. The accuracy, precision, recall, F1 score, ROC, computing time, and error rate are among the performance metrics used to assess the effectiveness of the suggested SINN-HAR-WSD-PEO strategy. In contrast to previous approaches, such as CNN-GRU-HAR-WSD, which uses wearable sensor data for deep learning-based human activity recognition (HAR), and CNN-BILSTM-HAR-WSD, which uses a multiple branch CNN-BiLSTM model for employing wearable sensor data for human activity recognition, the suggested SINN-HAR-WSD-PEO method achieves higher accuracy, recall, and precision, which were 22.36%, 25.42%, and 18.27%, 21.36%, 26.42%, and 18.27%, and a multi-task deep learning approach for sensor-based human activity recognition and segmentation (MDNN-SB-HAR) was 21.36%, 22.42%, and 19.27%, respectively.

人类活动识别(HAR)是行为分析、计算机通信和广泛技术研究的一个重要领域。深度学习(DL)方法被有效地应用于利用来自手机和可穿戴传感器的周期性信息来预测人类行为。基于dl的方法在识别活动方面非常出色,但在处理时间序列数据时遇到困难。本文提出了一种基于菲律宾鹰优化统计注入神经网络(WSN-HAR-PEO-SINN)的可穿戴传感器网络驱动的人体活动识别方法。首先,从无线传感器数据挖掘(WISDM)数据集收集输入信息。在预处理中,使用置信分割采样滤波(CPSF)将平面文件组合成一个数据帧。预处理后,使用unpaired multiview graph clustering (UMGC)对数据进行分割。它被用来分割人类活动的数量。分割后的输出通过二次相位s变换(QPST)发送到特征提取系统,该系统利用二次相位s变换收集局部特征,如偏度、平均值和偏离平均值、能量、梅尔频倒谱系数和熵。然后,将提取的特征输入到一个称为统计信息神经网络(SINN)的神经连接中,使用可穿戴传感器数据识别人类活动,并将数据分类为步行导向、手部导向活动一般和手部导向活动进食。一般来说,SINN不包含任何优化技术适应来识别保证精确识别人类活动的最优参数。为了增强SINN方法的权值参数,提出了菲律宾鹰优化(Philippine eagle optimization, PEO)方法。准确度、精密度、召回率、F1分数、ROC、计算时间和错误率是用来评估建议的SINN-HAR-WSD-PEO策略有效性的性能指标。与利用可穿戴传感器数据进行深度学习人体活动识别(HAR)的CNN-GRU-HAR-WSD和利用多分支CNN-BiLSTM模型利用可穿戴传感器数据进行人体活动识别的CNN-BiLSTM -HAR- wsd方法相比,所提出的SINN-HAR-WSD-PEO方法具有更高的正确率、召回率和精密度,分别为22.36%、25.42%和18.27%、21.36%、26.42%和18.27%。基于传感器的多任务深度学习人体活动识别与分割方法(MDNN-SB-HAR)分别占21.36%、22.42%和19.27%。
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引用次数: 0
A MILP-Based Optimization Framework for Joint Transmission Scheduling and Dynamic Channel Allocation in QoS-Constrained IoT Networks 基于milp的qos约束物联网联合传输调度与动态信道分配优化框架
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-16 DOI: 10.1002/dac.70267
Neda Mazloomi, Sajad Haghzad Klidbary, Arash Zaretalab

The growing scale of Internet of Things (IoT) networks poses critical challenges for delay-sensitive communication under strict Quality of Service (QoS) requirements. This paper presents MOCHA, a Mixed-Integer Linear Programming (MILP)-based optimization framework that jointly addresses transmission scheduling and dynamic channel allocation. A tailored Branch-and-Bound algorithm is developed to efficiently explore feasible solutions while ensuring capacity, exclusivity, and deadline constraints. Simulation results demonstrate that MOCHA achieves up to 30% lower transmission delay than leading approaches (MPSORP, MO-DRL, PQTBA). These results highlight the practicality of exact optimization in enabling reliable and delay-aware IoT services such as smart healthcare and autonomous systems.

物联网(IoT)网络规模的不断扩大,对具有严格服务质量(QoS)要求的延迟敏感通信提出了严峻的挑战。本文提出了一种基于混合整数线性规划(MILP)的优化框架MOCHA,该框架共同解决了传输调度和动态信道分配问题。开发了一种量身定制的分支定界算法,在保证容量、排他性和截止日期约束的同时,有效地探索可行的解决方案。仿真结果表明,与MPSORP、MO-DRL、PQTBA等主流方法相比,MOCHA的传输延迟降低了30%。这些结果突出了精确优化在实现可靠和延迟感知物联网服务(如智能医疗保健和自主系统)方面的实用性。
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引用次数: 0
Optimizing MIMO-GFDM for 5G and Beyond: A Joint Approach With Subcarrier Index Modulation and Constellation Precoding 优化MIMO-GFDM在5G及以后的应用:子载波索引调制和星座预编码的联合方法
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-13 DOI: 10.1002/dac.70236
R. Anil Kumar, Vivek Rajpoot

In the future, massive growth in mobile users in various applications is anticipated. To cope with this growth, the enhanced radio access technology (eRAT) can provide a solution to improve diversity gain and spectral efficiency. The state-of-the-art generalized frequency division multiplexing (GFDM) modulation techniques are combined with a multi-input multi-output (MIMO) antenna system to achieve better resource allocation, out-of-band emission, and signal strengths over the existing orthogonal multiple access (OMA) techniques. In the proposed research paper, the subcarrier index modulation (SIM) and constellation precoding (CP) techniques are combined with the MIMO-GFDM system to improve the performance of diversity gain, spectral efficiency, and energy efficiency. The system is called the SIM-CP-MIMO-GFDM. In the SIM-CP-MIMO-GFDM system, first, a few subcarriers are activated by index modulation bits in quadrature/in-phase dimensions, and then data symbols are constellation precoded. At the receiving end, the data blocks are detected using QR decomposition-based maximum likelihood (ML) detection. Finally, the paper incorporates theoretical and simulation result analysis and compares the proposed SIM-CP-MIMO-GFDM system with existing systems. The proposed SIM-CP-MIMO-GFDM outperforms the existing systems.

在未来,预计各种应用程序的移动用户将大幅增长。为了应对这种增长,增强型无线电接入技术(eRAT)可以提供一种提高分集增益和频谱效率的解决方案。最先进的广义频分复用(GFDM)调制技术与多输入多输出(MIMO)天线系统相结合,以实现比现有的正交多址(OMA)技术更好的资源分配、带外发射和信号强度。本文将子载波指数调制(SIM)和星座预编码(CP)技术与MIMO-GFDM系统相结合,提高了分集增益、频谱效率和能量效率。该系统称为SIM-CP-MIMO-GFDM。在SIM-CP-MIMO-GFDM系统中,首先通过正交/同相维的索引调制位激活少量子载波,然后对数据符号进行星座预编码。在接收端,使用基于QR分解的最大似然(ML)检测来检测数据块。最后,结合理论和仿真结果分析,将所提出的SIM-CP-MIMO-GFDM系统与现有系统进行了比较。所提出的SIM-CP-MIMO-GFDM系统优于现有系统。
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引用次数: 0
Reliable Communication Through Energy-Efficient Congestion Control Mechanism Utilizing Deep Learning and Meta-Heuristic Optimization in Wireless Multimedia Sensor Networks 基于深度学习和元启发式优化的无线多媒体传感器网络节能拥塞控制机制的可靠通信
IF 1.8 4区 计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2025-09-13 DOI: 10.1002/dac.70254
Moses Angeline Felicia, Rajamani Samson Ravindran, Hendry Lilly Beaulah, Thangaraj Kesavan

Wireless Multimedia Sensor Networks (WMSNs) are plagued with issues of battery life limitation and congestion, which lead to packet loss, energy consumption, and delay. In this paper, an energy-aware congestion control architecture named OCNN-TDO-WMSN, combining Orthogonal Convolutional Neural Networks (OCNN) and Tasmanian Devil Optimization (TDO), is proposed. The system uses a rate-based congestion control with cluster routing to reduce energy and delay. Double Fuzzy Clustering-driven Context Neural Networks (DFCCNN) are employed for clustering, OCNN for cluster head election, and TDO for adaptive packet rate adaptation. Throughput is additionally enhanced through the use of a Semi-Decentralized Energy Routing Algorithm (SDERA). The proposed method is simulated in NS-3 and is tested with parameters of energy consumption, lifetime, delay, throughput, packet delivery ratio (PDR), and reliability. Results indicate that OCNN-TDO-WMSN reaches a throughput of 50 Kbps and a PDR of 95%, better than the current methods.

无线多媒体传感器网络(wmsn)受到电池寿命限制和拥塞问题的困扰,这些问题会导致丢包、能耗和延迟。本文将正交卷积神经网络(OCNN)与塔斯马尼亚魔鬼优化(TDO)相结合,提出了一种能量感知的拥塞控制体系结构OCNN-TDO- wmsn。该系统使用基于速率的拥塞控制和集群路由,以减少能量和延迟。双模糊聚类驱动上下文神经网络(DFCCNN)用于聚类,OCNN用于簇头选举,TDO用于自适应包速率自适应。通过使用半分散能量路由算法(SDERA),吞吐量也得到了提高。在NS-3中对该方法进行了仿真,并用能耗、寿命、时延、吞吐量、包投递率(PDR)和可靠性等参数进行了测试。结果表明,OCNN-TDO-WMSN的吞吐量为50 Kbps, PDR为95%,优于现有的方法。
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引用次数: 0
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International Journal of Communication Systems
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